Sgrò Francesco, Quinto Antonella, Lipoma Mario, Stodden David
KG4SPA-Kore Research Group for Sport and Physical Fitness Assessment, 94100 Enna, Italy.
Department of Human and Society Sciences, University of Enna "Kore", 94100 Enna, Italy.
J Funct Morphol Kinesiol. 2024 Feb 6;9(1):29. doi: 10.3390/jfmk9010029.
This study aimed to assess which multidimensional performance indexes were the best predictors of talent identification in volleyball. Fifty-five female players (age: 13.8 ± 1.81 years; mass: 55.12 ± 8.12 kg; height: 158.23 ± 7.62 cm) were clustered into two groups according to some physical characteristics (i.e., the first group included players with more favorable performance predictors). Musculoskeletal Fitness (MSF), Functional Motor Competence (FMC), and Declarative Tactical Knowledge (DTK) were measured as multidimensional indexes of performance. Moderate-to-large differences between groups were found for each index in favor of the first group. Regression analyses were performed to examine the variance explained by MSF, FMC, and DTK in the two groups. A model with FMC components explained slightly more variance in the group predictor variables ( = 0.53) than a model using only MSF components ( = 0.45). Among FMC components, the score of the Throw-and-Catch test resulted in the best predictor (Odds Ratio = 1.58) for determining group selection, followed by the score of the Supine-to-Stand-and-Go test (Odds Ratio = 0.02). An additional model composed by MSF and FMC significant predictors (i.e., functional fitness index) and DTK explained 63% of the variance ( = 0.63), and these were significant predictors of group membership (Odds Ratio = 6.32 and Odds Ratio = 1.51, respectively). A more comprehensive multidimensional analysis of youth performances is warranted to identify and monitor the best players in a youth volleyball context.
本研究旨在评估哪些多维表现指标是排球人才识别的最佳预测指标。55名女性运动员(年龄:13.8±1.81岁;体重:55.12±8.12千克;身高:158.23±7.62厘米)根据一些身体特征被分为两组(即第一组包括具有更有利表现预测指标的运动员)。肌肉骨骼适能(MSF)、功能性运动能力(FMC)和陈述性战术知识(DTK)被作为表现的多维指标进行测量。发现每组指标之间存在中等到较大的差异,有利于第一组。进行回归分析以检验MSF、FMC和DTK在两组中所解释的方差。一个包含FMC成分的模型在组预测变量中解释的方差(=0.53)比仅使用MSF成分的模型(=0.45)略多。在FMC成分中,抛接测试的分数是确定组选择的最佳预测指标(优势比=1.58),其次是仰卧起坐起身跑测试的分数(优势比=0.02)。一个由MSF和FMC显著预测指标(即功能性适能指数)以及DTK组成的附加模型解释了63%的方差(=0.63),并且这些是组成员身份的显著预测指标(优势比分别为6.32和1.51)。有必要对青少年表现进行更全面的多维分析,以识别和监测青少年排球环境中的最佳运动员。